title: Effects of study precision and risk of bias in networks of interventions: a network meta-epidemiological study.
abstract: BACKGROUND
  Empirical research has illustrated an association between study size and relative treatment effects, but conclusions have been inconsistent about the association of study size with the risk of bias items. Small studies give generally imprecisely estimated treatment effects, and study variance can serve as a surrogate for study size.
  
  
  METHODS
  We conducted a network meta-epidemiological study analyzing 32 networks including 613 randomized controlled trials, and used Bayesian network meta-analysis and meta-regression models to evaluate the impact of trial characteristics and study variance on the results of network meta-analysis. We examined changes in relative effects and between-studies variation in network meta-regression models as a function of the variance of the observed effect size and indicators for the adequacy of each risk of bias item. Adjustment was performed both within and across networks, allowing for between-networks variability.
  
  
  RESULTS
  Imprecise studies with large variances tended to exaggerate the effects of the active or new intervention in the majority of networks, with a ratio of odds ratios of 1.83 (95% CI: 1.09,3.32). Inappropriate or unclear conduct of random sequence generation and allocation concealment, as well as lack of blinding of patients and outcome assessors, did not materially impact on the summary results. Imprecise studies also appeared to be more prone to inadequate conduct.
  
  
  CONCLUSIONS
  Compared to more precise studies, studies with large variance may give substantially different answers that alter the results of network meta-analyses for dichotomous outcomes.
authors.affiliation: Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece, Department of Orthopaedics, School of Medicine, University of Ioannina, Ioannina, Greece, Molecular Cell Biology and Regenerative Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, Department of Orthodontics and Dentofacial Orthopedics, Dental School, Medical Faculty, University of Bern, Bern, Switzerland, Private practice, Corfu, Greece, Center for Evidence Based Medicine and Department of Biostatistics, Program in Public Health, Brown University, Providence, RI, USA and School of Social and Community Medicine, University of Bristol, Bristol, UK.
authors.forename: Anna
authors.initials: A
authors.lastname: Chaimani
authors.forename: Haris S
authors.initials: HS
authors.lastname: Vasiliadis
authors.forename: Nikolaos
authors.initials: N
authors.lastname: Pandis
authors.forename: Christopher H
authors.initials: CH
authors.lastname: Schmid
authors.forename: Nicky J
authors.initials: NJ
authors.lastname: Welton
authors.forename: Georgia
authors.initials: G
authors.lastname: Salanti
mesh_terms: D001499:Bayes Theorem; D015982:Bias; D016021:Epidemiologic Studies; D008390:Markov Chains; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity
keywords: Multiple-treatments meta-analysis; indirect comparison; mixed-treatment comparison; publication bias; small-study effects